## Tuesday, February 27, 2018

### ResearchMethods: Operationalization: Levels of Measurement (W8-P2) Sp18

As you are determining what your variables are and how you are going to measure them, it is also helpful to have clearly in mind what type of data (or level of measurement) you will be using.  This is especially helpful when you are doing statistical analysis on the data later in the research process.

Recall the earlier discussion of types of variables?  Nominal variable and ordered variables, right?
Now, let's expand that "ordered" type to get a total of four types of variables or levels of measurement.

The above video covers nominal, ordinal and interval.  Note the addition of ratio below.  What's the difference between interval and ration?

 Level Can be Ranked? Equal Distance Zero-Point Example Variables Nominal No N/A N/A Gender Ordinal Yes No N/A List of most preferred TV shows Interval Yes Yes Arbitrary Has + & - Agreement on Likert-Scale Ratio Yes Yes Absolute 0 = absence Amount of time talking

Nominal level:
• nominal variables are classified into categories (names)
• They are not arranged in any particular order
• e.g., frequency counts, percentages.
• 48% male and 52% female
• 32% Catholic, 20% Baptist, etc.
Ordinal level:
• categories are ordered from highest to lowest
• intervals between categories are not standardized
• e.g., frequency counts, percentages
Interval level:
• categories are ranked
• assumed equal distances between ranks
• Arbitrary zero-point
• e.g. temperature - 0 degrees doesn’t mean the absence of temperature. Scale has + & - values.
• Another example: Likert-Scale
Ratio Level:
• categories are ranked
• Equal distances between rank
• Absolute Zero point.   Zero means the absence of the thing you are measuring and there is no negative value.
• e.g.,  age, weight, number of words in a sentence, etc.

What is the connection between a horse race and levels of measurement?

Photo used under Creative Commons.

How would the MythBusters research (viewed earlier) fit in here?  Did they operationalize their variables?  How? At what level?

Note: The level of measurement (or kind/type of data) you have will determine what statistics you use.  More on this later.

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